Structural identification of Egnatia Odos bridges based on ambient and earthquake induced vibrations

The dynamic characteristics of two representative R/C bridges on Egnatia Odos motorway in Greece are estimated based on low amplitude ambient and earthquake-induced vibrations. The present work outlines the instrumentation details, algorithms for computing modal characteristics (modal frequencies, damping ratios and modeshapes), modal-based finite element model updating methods for estimating structural parameters, and numerical results for the modal and structural dynamic characteristics of the two bridges based on ambient and earthquake induced vibrations. Transverse, bending and longitudinal modes are reliably identified and stiffness-related properties of the piers, deck and elastomeric bearings of the finite element models of the two bridges are estimated. Results provide qualitative and quantitative information on the dynamic behavior of the bridge systems and their components under low-amplitude vibrations. Modeling assumptions are discussed based on the differences in the characteristics identified from ambient and earthquake vibration measurements. The sources of the differences observed between the identified modal and structural characteristics of the bridges and those predicted by finite element models used for design are investigated and properly justified

International Consensus Based Review and Recommendations for Minimum Reporting Standards in Research on Transcutaneous Vagus Nerve Stimulation (Version 2020).

Given its non-invasive nature, there is increasing interest in the use of transcutaneous vagus nerve stimulation (tVNS) across basic, translational and clinical research. Contemporaneously, tVNS can be achieved by stimulating either the auricular branch or the cervical bundle of the vagus nerve, referred to as transcutaneous auricular vagus nerve stimulation(VNS) and transcutaneous cervical VNS, respectively. In order to advance the field in a systematic manner, studies using these technologies need to adequately report sufficient methodological detail to enable comparison of results between studies, replication of studies, as well as enhancing study participant safety. We systematically reviewed the existing tVNS literature to evaluate current reporting practices. Based on this review, and consensus among participating authors, we propose a set of minimal reporting items to guide future tVNS studies. The suggested items address specific technical aspects of the device and stimulation parameters. We also cover general recommendations including inclusion and exclusion criteria for participants, outcome parameters and the detailed reporting of side effects. Furthermore, we review strategies used to identify the optimal stimulation parameters for a given research setting and summarize ongoing developments in animal research with potential implications for the application of tVNS in humans. Finally, we discuss the potential of tVNS in future research as well as the associated challenges across several disciplines in research and clinical practice

Information-driven modeling of structures using a Bayesian framework

This work presents a comprehensive Bayesian framework for integrating information from data and models of civil infrastructure systems. In the proposed framework, modeling uncertainties are quantified and propagated through simulations using probability tools. Bayes theorem is used to select the most appropriate model among alternative competing ones, to estimate the parameters of a model and the uncertainties in the parameters, and to propagate the uncertainties in output quantities of interest that are important for evaluating structural performance and safety. The framework is developed using as data the modal characteristics estimated from response time history measurements. Theoretical challenges associated with the selection of the model prediction error equation introduced to build up the likelihood are pointed out. Bayesian tools such as Laplace asymptotic approximations and sampling algorithms require a moderate to very large number of system re-analyses to be performed, often resulting in excessive computational demands. Computationally efficient techniques are presented to drastically speed up computations within the Bayesian uncertainty quantification framework. These techniques include model reduction techniques based on component mode synthesis, surrogate models and parallelized Bayesian algorithms to exploit HPC environments. Bayesian optimal experimental design methods constitute a major component of the proposed framework for cost-effectively selecting the most informative data. A computationally efficient asymptotic approximation is proposed to simplify information-based utility functions used for optimizing the placement of sensors in a structure. The structure of the approximation provides insight into the use of the prediction error spatial correlation to avoid sensor clustering, as well as the effect of the prior uncertainty on the optimal sensor configuration. The framework is illustrated by integrating vibration measurements and high fidelity models for (a) a reinforced concrete bridge to update stiffness related model parameters, and (b) a circular hanger to estimate the axial tension required in structural safety evaluations. © Springer International Publishing AG 2018

A computationally efficient Bayesian framework for structural health monitoring using physics-based models

A Bayesian inference framework for structural damage identification is presented. Sophisticated structural identification methods, combining vibration information from the sensor network with the theoretical information built into a high-fidelity finite element model for simulating structural behaviour, are incorporated into the system in order to monitor structural condition, track structural changes and identify the location, type and extent of the damage. The methodology for damage detection combines the information contained in a set of measurement modal data with the information provided by a family of competitive, parameterized, finite element model classes simulating plausible damage scenarios in the structure. The computational challenges encountered in Bayesian tools for structural damage identification are addressed. Simulated modal data from the Metsovo Bridge are used to validate the effectiveness of the methodology. © Civil-Comp Press, 2015

Bayesian model-updating using features of modal data: Application to the Metsovo bridge

A Bayesian framework is presented for finite element model-updating using experimental modal data. A novel likelihood formulation is proposed regarding the inclusion of the mode shapes, based on a probabilistic treatment of the MAC value between the model predicted and experimental mode shapes. The framework is demonstrated by performing model-updating for the Metsovo bridge using a reduced high-fidelity finite element model. Experimental modal identification methods are used in order to extract the modal characteristics of the bridge from ambient acceleration time histories obtained from field measurements exploiting a network of reference and roving sensors. The Transitional Markov Chain Monte Carlo algorithm is used to perform the model updating by drawing samples from the posterior distribution of the model parameters. The proposed framework yields reasonable uncertainty bounds for the model parameters, insensitive to the redundant information contained in the measured data due to closely spaced sensors. In contrast, conventional Bayesian formulations which use probabilistic models to characterize the components of the discrepancy vector between the measured and model-predicted mode shapes result in unrealistically thin uncertainty bounds for the model parameters for a large number of sensors. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)

Structural health monitoring of a ravine bridge of Egnatia Motorway during construction

The instrumental rapid monitoring of the dynamic (ambient) response of a balanced cantilevered ravine bridge of Egnatia Motorway during its construction phases, when subjected to wind and other construction loads, was implemented. The aim is to verify the conformity both of the sequential construction phases and of the final completed structure of the ravine bridge of Metsovo to the design predictions. In this paper the modal frequencies, damping ratios and modeshape components of the completed balanced cantilever of pier M3 were identified from ambient acceleration records, and its analytical dynamic model was updated to determine the actual stiffness and mass properties of the structure

Effect of genotype and cutting type on the vegetative propagation of the pine hybrid (Pinus brutia (Ten) × Pinus halepensis (Mill))

Improved methods to propagate vegetatively selected individuals of the promising artificial pine hybrid Pinus brutia (Ten) x P halepensis (Mill) are required. Repeated spraying with 200 mg·l-1 BA or one spraying with 1 000 mg·l -1 of the herbicide Arsenal on the stems of 4-year-old seedlings, resulted in the production of the largest possible number of fascicle shoots. The fascicle shoots produced were taken as cuttings and were tested for rooting. In the rooting experiments the effect of genotype, cutting type, cutting size and auxin treatment were investigated. Genotype and cutting type proved to be the most crucial factors for rooting and clones with high rootability.L'effet du génotype et du type de bouture sur la multiplication végétative de l'hybride de pin (Pinus brutia (Ten) x Pinus halepensis (Mill)). L'amélioration des méthodes de multiplication végétative des individus sélectionnés de l'hybride artificiel de pin Pinus brutia (Ten) x Pinus halepensis (Mill) est nécessaire. Quatre différents régulateurs de croissance (TIBA, Alar, GA3, BA) ont été appliqués avec différentes combinaisons et concentrations sur la tige de plants de 4 ans de cet hybride artificiel afin d'obtenir l'induction de pousses interfasciculaires (tableau I). L'effet du meilleur traitement (200 mg·l-1 BA) de l'expérience a été comparé avec celui de l'herbicide Arsenal. La pulvérisation répétée de 200 mg·l-1 BA ou une pulvérisation de 1000 mg·l-1 d'Arsenal sur la tige de plants de 4 ans, conduit à la production du plus grand nombre des pousses interfasciculaires (tableau II, fig 1A). Ces pousses ont été utilisées comme boutures et étudiées pour leur enracinement. Dans cette expérience d'enracinement, on a analysé l'effet du génotype, du type de bouture, de la taille de la bouture et du traitement par l'auxine. Parmi les 8 clones testés, on a observé une grande variabilité en ce qui concerne l'enracinement (fig 2). Les boutures interfasciculaires se sont enracinées plus facilement et elles ont développé un meilleur système racinaire que celui des boutures de tige (fig 1B, 1 C). En ce qui conceme l'effet du génotype, on a trouvé que quelques clones s'enracinent facilement ou difficilement indépendamment de leur hauteur ou du traitement par l'auxine (tableau III). Les concentrations variées d'auxine (0, 4000, 8000 ppm K-IBA) influent différemment sur les 2 types de bouture (fig 3). Les plantules provenant des pousses interfasciculaires se caractérisent par leur vigueur et leur orthotropie (fig 1D)

System identification of a R/C bridge based on ambient vibrations and 3D numerical simulations of the entire soil-structure system

The scope of this paper is to identify the parameters affecting the dynamic response of an existing R/C bridge, based on low ambient amplitude vibration measurements and numerical predictions using complex finite element models. For this purpose, the instrumented, 2nd Kavala Bypass Ravine Bridge constructed along the Egnatia Motorway Greece is studied and a refined three- dimensional (3D) FEM is developed that takes into consideration the coupling and dynamic interaction of the overall superstructure-foundationsoil and deck-abutment-embankment system. The instrumentation schemes and the necessary algorithms applied for computing the modal characteristics of the bridge are discussed, while the modelling assumptions made for the soil-structure system are comparatively assessed and justified for various models of different levels of complexity. Given the large number of the system's degrees of freedom, a manual, modal-based FEM updating method is also presented. The results show good agreement between the measured and computationally predicted dynamic characteristics of the structure. They also show that the accurate estimation of the pier, deck and bearings stiffness is a key parameter for reliable system identification

Model calibration of metsovo bridge using ambient vibration measurements from various construction phases

Available methods for structural model updating are employed to develop high fidelity models of the soil-foundation-structure of Metsovo bridge using ambient vibration measurements. The Metsovo bridge, the highest bridge of the Egnatia Odos Motorway, is a two-branch balanced cantilever ravine bridge. Ambient vibration measurements are available during different construction phases of the bridge. Operational modal analysis software is used to obtain the modal characteristics of the bridge for the various sets of vibration measurements. The modal characteristics are then used to update a detailed numerical model of the bridge based on solid finite elements. A Bayesian method for parameter estimation is used for estimating the parameters of the soil-foundation-structure models under the different construction phases. The inference is based on modal properties identified using the experimental measurements from a number of sensor configuration setups. HPC based on the Transitional MCMC technique, integrated with model reduction tools and surrogate XTMCMC techniques, are used to carry out the Bayesian parameter inference. The discrepancies in the identified parameters using ambient vibrations under the different construction phases are discussed. The effectiveness of the updated models and their predictive capabilities are assessed